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Predicting the Crushing Behavior of Axially Loaded Elliptical Composite Tubes Using Artificial Neural Networks

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Abstract

In this research work, the artificial neural networks (ANN) technique is used in predicting the crushing behavior and energy absorption characteristics of axially-loaded glass fiber/epoxy composite elliptical tubes. Predictions are compared to actual experimental results obtained from the literature and are shown to be in good agreement. Effects of parameters such as network architecture, number of hidden layers and number of neurons per hidden layer are also considered. The study shows that ANN techniques can effectively be used to predict the crushing response and the energy absorption characteristics of elliptical composite tubes with various ellipticity ratios subjected to axial loading.

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Acknowledgment

The author would like to thank Dr. El-Sadig Mahdi, Associate Professor in the Kulliyyah of Engineering at the International Islamic University in Malaysia for providing the experimental data used in this work.

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  1. Mechanical Engineering Department, American University of Sharjah, Sharjah, United Arab Emirates

    Hany El Kadi

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  1. Hany El Kadi
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Correspondence to Hany El Kadi.

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El Kadi, H. Predicting the Crushing Behavior of Axially Loaded Elliptical Composite Tubes Using Artificial Neural Networks. Appl Compos Mater 15, 273–285 (2008). https://doi.org/10.1007/s10443-008-9074-2

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  • DOI: https://doi.org/10.1007/s10443-008-9074-2

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